Patterns detected in computation subgraphs:
The next level of granularity in explainable AI is to detect patterns in computation graphs according to the utilization-based class encodings as shown in Figure REF  (right). The motivation comes from the initial reports about semantically meaningful outputs of a group of computation units in AI models (e.g., partial curve detectors [1]}) and a hypothesis that such groups would repeat to encode more complex curves.
The problem lies in finding subgraphs in an AI computation graph (AI model) that are utilized the same way. This problem is known to be NP-hard (non-deterministic polynomial-time hardness) [2]}.
